Anatomical tree structures commonly exist in human bodies, including human airways, blood vessels (such as arteries, veins, capillaries, etc.), nervous tissues, and breast ducts extending from the nipple, etc. Recent technological advances in medical imaging (CT, MRI, DSA imaging, etc.) make it possible to non-invasively acquire medical images of different dimensions, such as 2D, 3D, 4D, etc., containing the anatomical tree structure. Clinicians rely on radiologists' interpretation of the medical images to perform various disease diagnosis, including but not limited to abnormality detection (such as lumen stenosis/widening detection, calcification detection, plaque detection, etc.), abnormality classification (such as plaque type classification among normal, stenosis, widening, calcified plaque, non-calcified plaque and mixed plaque, etc.), parameter quantification (such as abnormality (narrowing, widening, calcification) degree quantification, physiological measurement (diameter, area, flow rate, etc.) estimation, fractional flow reserve estimation), tree branch labeling (such as labeling the extracted branches with their anatomical names), and segmentation (such as vessel lumen segmentation), etc.
Usually, in clinical practice, the anatomical tree structure analysis is manually performed by a radiologist, which is labor-intensive and time-consuming, and the results may be subjective. Therefore, automated/semi-automated computer implemented anatomical tree structure analysis may be adopted to assist the radiologists in improving the efficiency, accuracy, and consistency of the image analysis.
Although machine learning-based algorithms have been introduced for such semi-automated or automated image analysis of the anatomical tree structure, these algorithms typically rely on the local features of a single centerline point or sequential centerline points sampled along individual branches, and thus are only able to achieve single point or sequential analysis. More importantly, for the same anatomical tree structure, these algorithms have to analyze the respective branches asynchronously, which may obtain inconsistent analysis results in the bifurcation regions and overlapped branch regions, reducing the analysis accuracy and efficiency.
The present disclosure is proposed to address the above concerns.